The promotion of skin-tone diversity in AI requires collaboration, commitment, and vigilantism. The failure to address these biases can have detrimental consequences, leading to further marginalization and discrimination.
AI technology is aiming to further combat societal biases in its next generations of visual technology. Promoting skin-tone diversity and combating bias in AI requires a multifaceted approach. Here are some key steps and considerations to ensure AI respects and promotes skin-tone diversity:
Data Collection & Diversity:
- Inclusivity: Datasets used to train AI, especially in facial recognition and related areas, should have diverse and representative examples covering all skin tones.
- Avoiding Bias: Be vigilant about potential biases. A dataset that predominantly features light-skinned individuals could make the AI more accurate for that group, neglecting darker-skinned individuals.
- Different Conditions: Test the algorithm in various conditions (different lighting, backgrounds, etc.) to ensure that it doesn’t have accuracy disparities based on skin tone.
- Feedback Loop: Incorporate user feedback, especially from marginalized communities, to make necessary adjustments.
- Open the Black Box: While proprietary considerations might prevent complete transparency, it’s crucial to provide enough insight so that third-party experts can assess fairness and potential biases.
- Clarify Limitations: AI providers should communicate the limitations and potential biases of their products to users.
- Guidelines & Standards: Adhere to guidelines and standards that prioritize fairness, transparency, and diversity.
- Ethics Committees: Form independent ethics committees with a diverse membership to oversee and guide AI development and deployment.
Education & Training:
- AI Teams: Ensure that AI development and deployment teams are aware of issues related to skin-tone diversity.
- Ongoing Learning: Keep updated on research and methods that promote fairness in AI.
Regulation & Policy:
- Audits: Regularly audit algorithms for biases and make the findings available.
- Policy Advocacy: Support policies that prioritize fairness in AI. For example, some regions have banned or restricted certain uses of facial recognition technology due to concerns about racial and gender biases.
- Work with non-governmental organizations & Academia: Collaborate with non-profits, academia, and other organizations working on fairness in AI.
- User Input: Engage with the user community, especially marginalized groups, to gain insights and feedback.
Awareness & Outreach:
- Awareness Campaigns: Conduct campaigns that educate the public about AI’s potential biases and the importance of skin-tone diversity.
- Open Dialogues: Engage in open dialogues about challenges and strategies to promote skin-tone diversity in AI.
Tools & Techniques:
- Bias Detection: Use tools that can automatically detect and mitigate biases in datasets and algorithms.
- Continuous Improvement: Regularly retrain AI models as more diverse data becomes available.
- Ensure that interfaces, feedback mechanisms, and user experiences are designed keeping in mind the diverse user base.
The promotion of skin-tone diversity in AI is a continuous process that requires commitment, vigilance, and collaboration. The consequences of not addressing these biases can be grave, leading to discrimination, mistrust, and further marginalization of already underrepresented communities. On the positive side, a focus on inclusivity can lead to better AI products that serve a broader range of users effectively and ethically.
Jan Iverson is Head of Studio at FS Studio and an award-winning product leader with over 20-years of extensive experience in digital media and marketing, with a specialization in the design and development of AR, VR and 3D activations: mobile apps, games, LBE, sales tools, digital twins; with XR cross-platform content development, and a track record of success in leading award-winning digital creative teams. Virtually Human is her bi-weekly series